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PRISM: Análisis de enfermedades raras que preservan la privacidad utilizando cifrado totalmente homomórfico

Güliz Akkaya1, Nesli Erdoğmuş1, Mete Akgün2,3

  • 1Department of Computer Engineering, İzmir Institute of Technology, Izmir, Turkey.

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Resumen

PRISM, un marco de preservación de la privacidad que utiliza cifrado totalmente homomórfico (FHE), permite el análisis colaborativo de variantes de enfermedades raras en todas las instituciones sin exposición de datos. Ofrece un filtrado más rápido para las variantes genéticas bajo varios modelos de herencia.

Palabras clave:
Cifrado homomórficoprotección de la intimidadEnfermedades raras

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Área de la Ciencia:

  • La genómica
  • La bioinformática
  • Tecnologías para preservar la privacidad

Sus antecedentes:

  • Las enfermedades raras afectan a millones en todo el mundo, pero el análisis genómico se ve obstaculizado por los datos limitados y las estrictas regulaciones de privacidad como GDPR.
  • El análisis colaborativo de datos genómicos es crucial para comprender los mecanismos de las enfermedades raras, pero enfrenta importantes desafíos de privacidad.

Objetivo del estudio:

  • Introducir PRISM, un nuevo marco de conservación de la privacidad para el análisis de variantes de enfermedades raras.
  • Permitir una colaboración segura entre instituciones sobre datos genómicos sensibles.
  • Facilitar el filtrado de variantes a través de modelos de herencia recesivos, dominantes y de novo sin exposición de datos.

Principales métodos:

  • Desarrollo de PRISM, un marco que utiliza cifrado totalmente homomórfico de umbral (FHE) para la gestión descentralizada de claves.
  • Implementación de dos variantes algorítmicas: intensivas en la multiplicación (MUL-IN) y en la adición (ADD-IN).
  • Filtración de las variantes causantes de enfermedades en los datos cifrados mediante FHE.

Principales resultados:

  • PRISM permite el análisis de variantes de enfermedades raras en múltiples instituciones al tiempo que preserva la privacidad de los datos.
  • Los algoritmos ADD-IN logran mejoras significativas en el tiempo de ejecución (hasta 17 veces para recesivo / dominante, 22 veces para de novo) en comparación con MUL-IN.
  • El marco demuestra la practicidad y la escalabilidad para analizar millones de variantes en un entorno de nube única.

Conclusiones:

  • PRISM ofrece una solución innovadora para el análisis genómico de enfermedades raras que preserva la privacidad.
  • El marco supera las limitaciones de acceso a los datos impuestas por las regulaciones de privacidad.
  • Este estudio es pionero en el uso de FHE para el análisis de variantes de enfermedades raras en modelos de herencia múltiple.